Holoscan Sample App Data for AI-based Endoscopy Tool Tracking

This resource contains the convolutional LSTM model for tool tracking in laparoscopic videos by Nwoye et. al [1], and a sample surgical video.

\n
[1] Nwoye, C.I., Mutter, D., Marescaux, J. and Padoy, N., 2019. Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos. International journal of computer assisted radiology and surgery, 14(6), pp.1059-1067
\n

Model

The AI model for instrument tracking in endoscopy is an LSTM model which given an RGB image of 854 x 480 provides

per-instrument detection probability,
per-instrument detected tooltip location,
semantic segmentation of the instruments.\nEach pixel stores 7 labels.

Note: The provided model is in ONNX format. It will automatically be converted into a TensorRT model (.engine) the first time it is processed by a Holoscan application.

Inputs

data_ph:0 - Input RGB image ( batchsize, height, width, channels)\n
- shape=[1, 480, 854, 3]
- dtype=float32
- range=[0, 255]
\n
cellstate_ph:0 - LSTM hidden state tensor 0\n
- shape=[1, 60, 107, 7]
- dtype=float32
\n
hiddenstate_ph:0 - LSTM hidden state tensor 1\n
- shape=[1, 60, 107, 7]
- dtype=float32
\n

Outputs

Model/net_states:0 - LSTM output state tensor. Output of frame N is input for frame N + 1.\n
- shape=[1, 60, 107, 7]
- dtype=float32
\n
Model/net_states:0 - LSTM hidden state tensor. Output of frame N is input for frame N + 1.\n
- shape=[1, 60, 107, 7]
- dtype=float32
\n
probs:0 - Per instrument detection probability\n
- shape=[1, 7]
- dtype=float32
- range=[0,1]
\n
Localize/scaled_coords:0 - Per-instrument (x,y) detected tooltip location\n
- shape=[1, 2, 7]
- dtype=float32
\n
Localize_1/binary_masks:0 - image with per instrument segmentation mask. Each pixel stores 7 labels\n
- shape=[1, 60, 107, 7]
- dtype=float32
\n

Video Data

The sample data, kindly provided by Research Group Camma, IHU Strasbourg and University of Strasbourg, is a surgical video that includes all 7 instruments classes supported by the tool tracking model. It's in a raw H264 format.

Note: the .h264 file must be converted into a GXF tensor file using the convert_video_to_gxf_entities.py script on GitHub to be used with the VideoStreamReplayer Holoscan operator.

License

Refer to the license agreement for use of the sample data.

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Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos. International journal of computer assisted radiology and surgery, 14(6), pp.1059-1067](https://link.springer.com/article/10.1007/s11548-019-01958-6)\n\n## Model\n\nThe AI model for instrument tracking in endoscopy is an LSTM model which given an RGB image of `854 x 480` provides\n- per-instrument detection probability,\n- per-instrument detected tooltip location,\n- semantic segmentation of the instruments.\nEach pixel stores 7 labels.\n\n_Note: The provided model is in ONNX format. It will automatically be converted into a TensorRT model (.engine) the first time it is processed by a Holoscan application._\n\n### Inputs\n\n- `data_ph:0` - Input RGB image ( batchsize, height, width, channels)\n - `shape=[1, 480, 854, 3]`\n - `dtype=float32`\n - `range=[0, 255]`\n- `cellstate_ph:0` - LSTM hidden state tensor 0\n - `shape=[1, 60, 107, 7]`\n - `dtype=float32`\n- `hiddenstate_ph:0` - LSTM hidden state tensor 1\n - `shape=[1, 60, 107, 7]`\n - `dtype=float32`\n\n### Outputs\n\n- `Model/net_states:0` - LSTM output state tensor. Output of frame `N` is input for frame `N + 1`.\n - `shape=[1, 60, 107, 7]`\n - `dtype=float32`\n- `Model/net_states:0` - LSTM hidden state tensor. Output of frame `N` is input for frame `N + 1`.\n - `shape=[1, 60, 107, 7]`\n - `dtype=float32`\n- `probs:0` - Per instrument detection probability\n - `shape=[1, 7]`\n - `dtype=float32`\n - `range=[0,1]`\n- `Localize/scaled_coords:0` - Per-instrument (x,y) detected tooltip location\n - `shape=[1, 2, 7]`\n - `dtype=float32`\n- `Localize_1/binary_masks:0` - image with per instrument segmentation mask. Each pixel stores 7 labels\n - `shape=[1, 60, 107, 7]`\n - `dtype=float32`\n\n## Video Data\n\nThe sample data, kindly provided by [Research Group Camma](http://camma.u-strasbg.fr), [IHU Strasbourg](https://www.ihu-strasbourg.eu/) and [University of Strasbourg](https://en.unistra.fr/), is a surgical video that includes all 7 instruments classes supported by the tool tracking model. It's in a raw H264 format.\n\n_Note: the .h264 file must be converted into a GXF tensor file using the `convert_video_to_gxf_entities.py` script on [GitHub](https://github.com/nvidia-holoscan/holoscan-sdk/tree/main/scripts#convert_video_to_gxf_entitiespy) to be used with the `VideoStreamReplayer` Holoscan operator._\n\n## License\n\nRefer to the [license agreement](https://developer.nvidia.com/clara-holoscan-EULA) for use of the sample data.","Endoscopy Sample App Data",["D",1681919746426],"/api/image/nvidia/clara-holoscan/resources/holoscan_endoscopy_sample_data",49571144,"bias","explainability","privacy","safetyAndSecurity",[],[271,272,273,274,275],[],[],[],{"_92":291,"_124":292,"_125":293,"_127":128},{"_92":288,"_124":289,"_125":290,"_127":128},{"_92":284,"_124":285,"_125":286,"_127":128},{"_92":280,"_124":281,"_125":282,"_127":128},{"_92":276,"_124":277,"_125":278,"_127":128},"Holoscan","pltfm_clara_holoscan",{"_129":279,"_124":277},"platform","Healthcare","indus_healthcare",{"_129":283,"_124":281},"industry","DL","soln_dl",{"_129":287,"_124":285},"technology","Clara","pltfm_clara",{"_129":279,"_124":289},"AI","soln_ai",{"_129":287,"_124":292},{"_156":1271,"_158":1272},"ngcApps",[1224],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1205":1206,"_1207":1208,"_1209":1210,"_1211":1212,"_1213":305,"_187":331,"_1214":1215,"_1216":51,"_1217":135,"_1218":135,"_1219":1220,"_1221":135,"_1222":1223},"amplitude",{"_1202":1203,"_844":-7,"_1204":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1196":1197,"_1198":1199,"_1200":1201},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789298290300,"1.15.0","defaultSearchSuggestions",{"_1084":228,"_1085":1086,"_1087":1088,"_1089":1090},"announcements",[],"consentState",{"_370":1074,"_1075":135,"_1076":26,"_1077":26,"_1078":1079},"consentConfig",{"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371},"showContactAdmin",1789298290301,[],[],[345,346,347,348,349,350,351],{"_40":55,"_42":66,"_44":65,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":65,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":65,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":65,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":65,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":65,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":65,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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